Pyroelectric Infrared Sensor Research Articles

Measurement uncertainty analysis of centering error of pyroelectric infrared sensor module

Pyroelectric Infrared (PIR) Sensor Modules which are widely used in order to detect heating sources passing their field of views, are non-imaging electronic optical (EO) systems, in which the output is considered to be random signal over time. Specifying optical axis position of such system is certainly essential and challenging. This paper presents the uncertainty analysis of a measurement of centering error of the PIR sensor module that is conducted in a setup using a modulated infrared radiating source. The analysis helps point out specific parameters, such as, the difference between background temperature and heating source temperature; modulating frequency, etc., which have greatest effect on the measurement accuracy of such EO system.

Hyperbolic function based hybrid consistent adaptive sequential DL algorithms for PIR sensor based intruder detection

This study demonstrates that a novel dictionary learning (DL) algorithm-based approach can be successfully used in conjunction with an indigenously developed hardware module, using four pyroelectric infrared (PIR) sensors, to enhance the performance of an intruder detection system. In this work, initially, a hyperbolic function based Consistent Adaptive Sequential Dictionary Learning (CAS-DL) algorithm has been proposed (HCAS-DL). Then another dictionary learning algorithm, called label consistency based K-SVD (LCK-SVD) is considered, where the label information of each dictionary atom has been introduced to enforce discriminability in sparse code. The objective function in LCK-SVD is formuated simultaneously considering the concept of label consistency (LC) constraints, reconstruction errors, and classification errors. Next, the newly proposed HCAS-DL is hybridized with LCK-SVD to form a novel version of hybrid consistent adaptive sequential dictionary learning approach, named here as the LC-HCAS-DL algorithm. Extensive experiments have been performed to establish the suitability of our proposed approach for the problem under consideration. The performance evaluation clearly establishes that the LC-HCAS-DL algorithm can achieve superior performance compared to recently proposed LC-MCAS-DL algorithm and other state-of-the-art algorithms for the intruder detection problem under consideration.

Exploiting the PIR Sensor Analog Behavior as Thermoreceptor: Movement Direction Classification Based on Spiking Neurons.

Pyroelectric infrared sensors (PIR) are widely used as infrared (IR) detectors due to their basic implementation, low cost, low power, and performance. Combined with a Fresnel lens, they can be used as a binary detector in applications of presence and motion control. Furthermore, due to their features, they can be used in autonomous intelligent devices or included in robotics applications or sensor networks. In this work, two neural processing architectures are presented: (1) an analog processing approach to achieve the behavior of a presynaptic neuron from a PIR sensor. An analog circuit similar to the leaky integrate and fire model is implemented to be able to generate spiking rates proportional to the IR stimuli received at a PIR sensor. (2) An embedded postsynaptic neuron where a spiking neural network matrix together with an algorithm based on digital processing techniques is introduced. This structure allows connecting a set of sensors to the post-synaptic circuit emulating an optic nerve. As a case study, the entire neural processing approach presented in this paper is applied to optical flow detection considering a four-PIR array as input. The results validate both the spiking approach for an analog sensor presented and the ability to retrieve the analog information sent as spike trains in a simulated optic nerve.

A Cost-Effective Fall-Detection Framework for the Elderly Using Sensor-Based Technologies

Falls are critical events among the elderly living alone in their rooms and can have intense consequences, such as the elderly person being left to lie for a long time after the fall. Elderly falling is one of the serious healthcare issues that have been investigated by researchers for over a decade, and several techniques and methods have been proposed to detect fall events. To overcome and mitigate elderly fall issues, such as being left to lie for a long time after a fall, this project presents a low-cost, motion-based technique for detecting all events. In this study, we used IRA-E700ST0 pyroelectric infrared sensors (PIR) that are mounted on walls around or near the patient bed in a horizontal field of view to detect regular motions and patient fall events; we used PIR sensors along with Arduino Uno to detect patient falls and save the collected data in Arduino SD for classification. For data collection, 20 persons contributed as patients performing fall events. When a patient or elderly person falls, a signal of different intensity (high) is produced, which certainly differs from the signals generated due to normal motion. A set of parameters was extracted from the signals generated by the PIR sensors during falling and regular motions to build the dataset. When the system detects a fall event and turns on the green signal, an alarm is generated, and a message is sent to inform the family members or caregivers of the individual. Furthermore, we classified the elderly fall event dataset using five machine learning (ML) classifiers, namely: random forest (RF), decision tree (DT), support vector machine (SVM), naïve Bayes (NB), and AdaBoost (AB). Our result reveals that the RF and AB algorithms achieved almost 99% accuracy in elderly fall-d\detection.

A Non-Contact Fall Detection Method for Bathroom Application Based on MEMS Infrared Sensors

The ratio of the elderly to the total population around the world is larger than 10%, and about 30% of the elderly are injured by falls each year. Accidental falls, especially bathroom falls, account for a large proportion. Therefore, fall events detection of the elderly is of great importance. In this article, a non-contact fall detector based on a Micro-electromechanical Systems Pyroelectric Infrared (MEMS PIR) sensor and a thermopile IR array sensor is designed to detect bathroom falls. Besides, image processing algorithms with a low pass filter and double boundary scans are put forward in detail. Then, the statistical features of the area, center, duration and temperature are extracted. Finally, a 3-layer BP neural network is adopted to identify the fall events. Taking into account the key factors of ambient temperature, objective, illumination, fall speed, fall state, fall area and fall scene, 640 tests were performed in total, and 5-fold cross validation is adopted. Experimental results demonstrate that the averages of the precision, recall, detection accuracy and F1-Score are measured to be 94.45%, 90.94%, 92.81% and 92.66%, respectively, which indicates that the novel detection method is feasible. Thereby, this IOT detector can be extensively used for household bathroom fall detection and is low-cost and privacy-security guaranteed.

Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence

Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect only the presence of a subject's motion in their field of view (FOV) without other information about the actual location, information from overlapping FOVs of multiple sensors can be useful for localization. This study introduces the PIRILS (pyroelectric infrared indoor localization system), in which the sensing signal processing algorithms are augmented by deep learning algorithms that are designed based on the operational characteristics of the PIR sensor. Expanding to the detection of multiple targets, the PIRILS develops a quantized scheme that exploits the behavior of an artificial neural network (ANN) model to demonstrate localization performance in tracking multiple targets. To further improve the localization performance, the PIRILS incorporates a data augmentation strategy that enhances the training data diversity of the target's motion. Experimental results indicate system stability, improved positioning accuracy, and expanded applicability, thus providing an improved indoor multi-target localization framework.

GAN-based sensor data augmentation: Application for counting moving people and detecting directions using PIR sensors

In indoor environments, such as smart homes, the number of occupants within the space and their moving directions can provide a rich set of contextual information about the surroundings and occupants themselves, which can enable systems to adapt their services according to the occupants’ situation. Therefore, significant effort has been devoted to the development of variable sensing systems and learning methods. In this study, we introduce a pyroelectric infrared (PIR) sensor-based sensing system for counting moving people and detecting directions using convolutional neural networks (CNNs) and generative adversarial networks (GANs). PIR output signals were collected from four multiple-subject scenarios: single-, two-, three-, and four-subject groups in the experiments. We propose a novel time sequence sensor data augmentation algorithm, namely, auxiliary-classifier conditional GAN. This algorithm embeds the input data to reflect the condition to which the generated data should be transformed and its class information to which the generated data should be classified. The algorithm aims to build a model that works well in cases where multiple people move together (like to occur less than the cases when a single person moves alone). The experimental results show that when compared with the original model without augmentation, our multitask learning model combined with the proposed sample augmentation method increases the precision of counting moving people by 7.9%, 9.7%, 26%, and 37.5% for the one-, two-, three-, and four-subject groups, respectively, when compared with the original model without augmentation.

Development of an Early Fire Detection Technique Using a Passive Infrared Sensor and Deep Neural Networks

Early detection of fire is key to mitigate fire related damages. This paper presents a differential pyro-electric infrared (PIR) sensor and deep neural networks (DNNs) based method to detect fire in real-time. Since the PIR sensor is sensitive to sudden body motions and emits a continuous time-varying signal, experiments are carried out to collect human and fire motions using a PIR sensor. These signals are processed using one-dimensional continuous wavelet transform to perform feature extraction. The corresponding wavelet coefficients are converted into RGB spectrum images that are then used as inputs for a deep convolutional neural network. Various pre-trained DNN architectures are adopted to train and identify the collected data for background (no motion), human motion, and fire categories: small quasi-static and spreading fires. Experimental results show that the ShuffleNet architecture yields the highest prediction accuracy of 87.8%. Experimental results for the real-time strategy which works at a speed of 12 frames-per-second show 95.34% and 92.39% fire and human motion detection accuracy levels respectively.

Chip-level design for intelligent human position detection based on PIR sensor

PurposeThis study aims to present a novel technique to localize the human position in a room, to manage people in a specified space.Design/methodology/approachIn this study, a real-time human sensing detection and smart lighting control was designed within a single silicon core. The chip has been successfully realized within 1.5 mm2 silicon area using TSMC 0.25 um process.FindingsThis chip can read the weak signal of pyroelectric infrared (PIR) sensor to find the position of human body in a dark room and then help control the smart lighting system for an intelligent surveillance system.Originality/valueThis chip presented the retriggering delay control to expand the LED lighting time infinitely to avoid lighting-off suddenly while users stay on a space. This function is very useful in a practical intelligent surveillance system that is mainly based on human detection to better reduce power dissipation and memory space.

Voice Controlled Car using Pyroelectric Infrared Sensor

In the era of emerging technology, Robotics with material handling is emerging now- a-days in our daily life. The techniques of material handling will diminish the physical efforts of humans. The count of handling tasks has been increased with the use of cost-efficient Bluetooth- controlled robot car. In this paper, for material handling we have designed and also implemented Bluetooth controlled robot car using pyroelectric infrared (PIR) sensor, Arduino, and Bluetooth module. This technique will lower the cost of the existing designs. Further, it will be beneficial to storage godowns, farmers, general public and educational organization as it will reduce the physical labor.

A Setup for Measuring the Centering Error of a Dual-Element Pyroelectric Infrared Sensor Module.

This paper presents an experimental setup to measure the horizontal centering error of a pre-built pyroelectric infrared (PIR) sensor module, in which a dual-element PIR sensor is aligned at the focal point of a single-zone Fresnel Lens. In the setup, the sensor module was placed facing a modulated infrared radiating source and turned over a range of horizontal angles. The position of the optical axis of the sensor module was determined based on the analysis of the output response of the sensor at turned angles. Thus, the horizontal centering error of the module is defined as the difference between the mechanical axis of the housing and the found optical axis. For the prebuilt sensor module, with the specific available equipment, the measurement of the centering error of the module achieved a resolution of 0.02 degrees.

Review of signal processing applications of Pyroelectric Infrared (PIR) sensors with a focus on respiration rate and heart rate detection

The article reviews signal processing applications of Pyroelectric Infrared (PIR) sensors with the main focus on the respiration and pulse rate detection. Respiration and pulse rates are vital signs that are routinely measured by healthcare providers to assess a human body's basic functions. In this paper, we review non-contact human respiratory rate and heart rate (pulse) estimation methods using the low-cost, contact-free, and non-invasive Pyroelectric Infrared (PIR) sensor. The time-varying sensor signal generated by the chest and subtle head movements are used to infer the respiratory rate and the heart rate, respectively. The PIR sensor can be mounted on the head rest of a chair or placed near a bed and can continuously estimate both the respiratory rate and the heart rate of a resting person at the same time. Experimental results indicate that a positive correlation higher than 95% between the heart rate estimates and the pulse oximeter measurements is achieved in our datasets. Similarly, a correlation coefficient of 95% is also achieved between the PIR-based respiratory rate estimates and the respiratory chest movements.

Cooperative Networked PIR Detection System for Indoor Human Localization.

Pyroelectric Infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems can be categorized as wearable and non-wearable systems, where the latter are also known as device-free localization systems. Since the binary PIR sensor detects only the presence of a human motion in its field of view (FOV) without any other information about the actual location, utilizing the information of overlapping FOV of multiple sensors can be useful for localization. In this study, a PIR detector and sensing signal processing algorithms were designed based on the characteristics of the PIR sensor. We applied the designed PIR detector as a sensor node to create a non-wearable cooperative indoor human localization system. To improve the system performance, signal processing algorithms and refinement schemes (i.e., the Kalman filter, a Transferable Belief Model, and a TBM-based hybrid approach (TBM + Kalman filter)) were applied and compared. Experimental results indicated system stability and improved positioning accuracy, thus providing an indoor cooperative localization framework for PIR sensor networks.

DESIGN OF AN AUTOMATIC STERILIZATION GATE TOOL USING PIR MOTION SENSOR

This automatic sterilization gate tool is a simple tool that changes the manual method to a systemized and more efficient way. This tool uses an infrared motion sensor in which there is a main component, namely the PIR (Pyroelectric Infra-Red) sensor, which is a material that reacts to radiation and movement in front of it. This tool also uses a 12v dc pump which is used to draw out the disinfectant liquid stored in the container. This gate tool is designed using the main material and some additional materials, the automatic gate can discharge about 11 seconds of liquid from objects or people that pass through the sensor range, 11 seconds is the minimum time for this tool to work while a distance of 6 meters is the range from where the sensor is located. From that time it proved that this gate tool has 100% accuracy and runs well. This gate tool is used every day, turned on from the morning, and turned off at night. Every 17.00 hours this gate tool is always replenished with supplies of disinfectant liquid. The automatic gate has helped the community to reduce the risk of transmission of the coronavirus, it also raises the awareness of the covid-19 pandemic. The whole system is implemented and is tested for real-time operation. It is found working satisfactorily. The gate tool can be further improved by adding a scanning device to perform tracing for passersby.

DeepPIRATES: A Training-Light PIR-Based Localization Method With High Generalization Ability

Pyroelectric infrared (PIR) sensors are much promising for device-free localization (DFL) due to their advantages of lower cost, low power consumption, and privacy protection. Most PIR-based localization methods usually assume some geometric models according to the detection principle of PIR sensors, which are however not accurate or robust due to the various cases of infrared radiation from human body, especially the case of multiple persons. Recently, deep learning is utilized in the PIR-based localization method (i.e. PIRNet Yang et al.) and well handles the complex infrared radiation even in the multi-person case. However, this method requires a high training cost, and has very weak generalization ability as it assumes the PIR sensors' deployment in the testing environment is same to the deployment in training environment. To reduce the training cost and achieve high generalization ability, in this paper, we propose a robust method DeepPIRATES, which can be directly utilized in various deployment scenarios without retraining. DeepPIRATES combines deep learning and a geometric model. Specifically, DeepPIRATES divides the localization task into two steps. The first step utilizes a neural network to estimate the azimuth changes of multiple persons to a PIR sensor. Then, DeepPIRATES utilizes the persons' azimuth changes to infer their locations based on a geometric model. Extensive experimental results show that DeepPIRATES can achieve similar localization accuracy as PIRNet, while does not require to be retrained when the sensor deployment changes.

Automatic control system of highway lights

This research provides new effective and cheap designed system for save electrical energy dissipation in all urban area roads and highways. The aim of this system is to minimizing the waste electrical power on highways and urban area roads. The designed system depends on two Arduino circuit types, master and slave. The master Arduino has an ability to detect the day light by light dependent resistor (LDR) sensor and cars movements by pyroelectric infrared (PIR) sensor, according to these conditions, the master will send a signal by XBee module works as transmitter to the following five slaves Arduino which are waiting for a signal and receive it by XBee module works as receiver to turn ON the lights for 5 minutes then OFF it if there is no car movement on the street. The system can mount directly to the highway lights. The system has been tested and applied on the street lights, the system works perfectly and slaves respond fastly and effectively to the master Arduino signal.

Enhancing PIR-Based Multi-Person Localization Through Combining Deep Learning With Domain Knowledge

Device-free localization (DFL) techniques based on pyroelectric infrared (PIR) sensors have attracted much attention due to their advantages of low cost, low power consumption, and privacy protection. However, existing PIR-based DFL methods still have a practical limitation that they usually require a high deployment density in multi-person scenarios. In this article, we propose a new PIR-based DFL method which requires a significantly lower deployment density in the multi-person scenario. The proposed method is based on deep learning. Especially, instead of directly utilizing the commonly used neural network architectures, we proposed a task-specific network architecture that integrates the domain knowledge of PIR sensors to improve its localization performance. Specifically, following the idea of modular learning, we design the proposed network architecture to contain two modules for counting the number of existing persons and another is for predicting their locations, respectively. Moreover, we also design the modules for person counting and localization to contain two secondary modules for different sub-tasks. Besides, to further improve the localization performance, we also propose two data augmentation strategies that aim at enhancing the training data diversity from the aspects of the moving speed of existing persons and the influence of surrounding noise. Through the proposed method, we succeed to remarkably reduce the deployment density of the traditional PIR-based methods by about 76%, while maintaining the localization accuracy.

Intruder Localization and Tracking Using Two Pyroelectric Infrared Sensors

In this paper, we introduce a method to estimate the range of an intruder and track its trajectory by utilizing the received signal strength of the heat flux for pyroelectric infrared (PIR) sensors. To this end, we first develop a mathematical model of the received heat flux signal strength and the corresponding PIR signal for a moving intruder. The algorithm uses only two PIR sensors and the geometry of the field of views (FOVs) to perform the estimation and tracking process without any knowledge of the intruder’s parameters. The tracking algorithm shows remarkable performance in estimating the intruder’s parameters. The intruder heat flux was accurately estimated even at large separation distances as was the intruder path angle. Finally, the intruder’s location was also very accurately estimated with sub-meter error for large separation distances.

PIR Sensor-Based AAL Tool for Human Movement Detection: Modified MCP-Based Dictionary Learning Approach

Development of sensor(s)-based, signal processing-aided, low-cost ambient-assisted living tools (AALs), essentially for assisting elderly people, home automation, and remote monitoring purposes, has become an important research domain. Within this domain, developing intelligent systems for human movement recognition in specific directions has become a very important problem statement. This article shows how a sophisticated, low-cost, integrated system can be developed using an indigenously developed hardware–software combine. The solution employs around four pyroelectric infrared (PIR) sensor based hardware systems coupled with a novel dictionary learning algorithm. The work successfully carries out the recently proposed multiple cluster pursuit (MCP)-algorithm-based dictionary learning for the human detection problem and then proposes a new variant of MCP algorithm, called modified MCP algorithm, for this purpose. Extensive real-life performance evaluations have been performed to demonstrate the suitability of MCP and the modified MCP algorithms for the problem under consideration.

A Comparative Analysis of Deep Learning and Machine Learning on Detecting Movement Directions Using PIR Sensors

Machine learning has played a significant role in building intelligent systems in the history of data science. In the recent paradigm where objects in the world will be connected with each other, commonly referred to as the Internet of Things (IoT), people begin to consider the challenges and opportunities to utilize the huge data sets generated, also referred to as Big data. One of the active research topics in dealing with the IoT’s big data is the practical feasibility of algorithms used in classical machine learning but also in a newly emerging branch, called deep learning. In this article, we demonstrate a quantitative analysis comparing performance between classical machine learning and deep learning algorithms with a human movement direction detecting application based on analog pyroelectric infrared (PIR) sensor signals. The sensing data acquisition and retrieval system is implemented with the open-source IoT software platforms based on the oneM2M standard. With the analog PIR data sets collected from 30 subjects, we perform experimental studies comparing classical machine learning and deep learning algorithms in terms of economic feasibility, scalability, generality, and real-time detection performance. The results show that classical machine learning shows better performance in real-time detection (i.e., with the sensing values within the first 0.5 s). In contrast, our simple deep learning model achieves about 90% accuracy for detecting moving directions even with the data sets from only three subjects and a single PIR sensor. Moreover, it could be applied to a larger number of subjects without updates.